We propose a new method of estimation in high-dimensional linear regression model. It allows for very weak distributional assumptions including heteroscedasticity, and does not require the knowledge of the variance of random errors. The method is based on linear programming only, so that its...

We propose an instrumental variables method for inference in high-dimensional structural equations with endogenous regressors. The number of regressors K can be much larger than the sample size. A key ingredient is sparsity, i.e., the vector of coefficients has many zeros, or approximate...

In this article we consider the estimation of the joint distribution of the random coefficients and error term in the nonparametric random coefficients binary choice model. In this model from economics, each agent has to choose between two mutually exclusive alternatives based on the observation...

Nous considérons dans cet article des modèles à choix binaires et coefficients aléatoires. Le but est d'estimer de manière nonparamétrique la densité du coefficient aléatoire. Il s'agit d'un problème inverse mal posé caractérisé par une transformation intégrale. Un nouvel estimateur...